Cluster analysis or clustering refers to a process through which a set of data points are organized into groups called clusters. Typically, the goal of clustering is to group the data such that data points belonging to the same cluster are more similar to each other than they are to data points belonging to other clusters. Such groupings may provide meaningful insights with respect to large data sets that may not be readily apparent from the raw data itself. Clustering has broad applicability in a variety of fields such as data mining, machine learning, psychology and other social sciences, image and signal processing, bioinformatics, data summarization, pattern recognition, and other statistical analysis. U.S. Pat. No. 7,590,642 describes various examples of performing k-means clustering to group a set of data points.